New Functionalities

product_variability <- function(prod,price_var=1){

    date_range <- function(date1,date2){        
        date1= format(as.Date(date1, format = "%Y-%m-%d"), "%b-%y")
        date2= format(as.Date(date2, format = "%Y-%m-%d"), "%b-%y")
        drange <- paste(date2,date1,sep="-")       
        return(drange)
    }

    price_range <- function(price_min,price_max){        
        prange <- paste(price_min,price_max,sep="-")     
        return(prange)
    }

    #Detect variability in col column
    variability <- function(df, col,product){
        df <- subset(df, !is.na(df[[col]]))
        var=0
        if (nrow(df)>1){
            row_value <- df[1, col]
            for (row in 2:nrow(df)){
                if(df[row, col]!= row_value) {
                row_value <- df[row, col]
                var <- var + 1   
                }                                                        
            }
        }
        return(var)
    }

    #Get prices for product ID
    p_prices<-product_prices(prod)

    #Get how many times the price changes over the date range
    p_var <- variability(p_prices,"price",prod)

    #Get more information if the price change more or equal to price_var times
    if (p_var >= price_var){

        retailer_id <- product(prod)$retailer_id
        product<-product(prod)$product
        total_samples<-nrow(p_prices)

        date_range <- date_range(p_prices$'time'[1],p_prices$'time'[total_samples])
        price_range <- price_range(min(p_prices$'price'),max(p_prices$'price'))

        #Check availability variability
        if (all(is.na(p_prices[['available']]))){
            av_var <- "NA"
        }
        else{    
            av_var <- variability(p_prices,"available",prod)
        }

        #Check promotions variability
        if (all(is.na(p_prices[['price_promotion']]))){
            prom_var <- "NA"
        }
        else{    
            prom_var <- variability(p_prices,"price_promotion",prod)
        }

        #Save the data and convert indo a dataframe
        output<-list(retailer_id,prod,product,price_range,p_var,av_var,prom_var,total_samples,date_range)       
        output<-as.data.frame(output)
        names(output) <- c('retailer_id','product_id','product','price_range','price_variability','avail_variability','prom_variability','total_samples', 'date_range')
    }
    else{
    output <- data.frame()
    }            
    return(output)
}

product_variability(587936)

product_prices(587936)

retailer_variability <- function(ret_id, price_var=1, n_products=10) {

    output=data.frame()
    var_not_found =c()

    #For each retailer ID, get randomly #n_products products and detect variability
    for (ret in ret_id){
        products <- c(retailer_products(ret) [['product_id']])
        prod_ids <- c(sample(products, n_products, replace=FALSE))

        for (prod in prod_ids){

            getprod <- (product_variability(prod,price_var))
            if (nrow(getprod)>0){
                output <- rbind(output,getprod)
            }
        }                
    }
    #Print details if no variability found
    if (nrow(output)>0){
        var_not_found <- ret_id[!(ret_id %in% output[['retailer_id']])]

        if (length(var_not_found)>1){
            cat("Nothing found for retailers: ",var_not_found, sep=" ")
        }
        else if (length(var_not_found)==1) {
            cat("Nothing found for retailer: ",var_not_found)
        }
    }
    else{
        print("No variability found")    
    }
    output              
}

retailer_variability(71)

retailer_variability(c(71,24,3,52))

feeling_lucky_variability <- function(n_retailers=5, price_var=1, n_products=10){

    #For all the retailers ID, get randomly #n_retailers retailers
    retailers <- retailer()[['retailer_id']]
    ret_id <- c(sample(retailers, n_retailers, replace=FALSE))

    #Check variability for all the randomly selected retailers
    retailer_variability(ret_id, price_var, n_products)    
}

feeling_lucky_variability()

library(plotly)

plot_prod_variability<- function(prod){

    #Get data about the product
    p_prices <- product_prices(prod)
    product <- product(prod)$product
    retailer <- product(prod)$retailer_id       

    #Build an interactive plot
    trace_0 <- p_prices[['price']]
    trace_1 <- p_prices[['price_promotion']]

    fig <- plot_ly(p_prices, x = p_prices[['time']])
    fig <- fig %>% add_trace(y = ~trace_0, name = 'price',mode = 'lines')
    fig <- fig %>% add_trace(y = ~trace_1, name = 'price promotion', mode = 'lines')
    fig <- fig %>%
    layout(autosize = F,
           width = 600,
           height = 350,
           margin = list(l=50, r=50, b=100, t=150, pad=4),
           title = list(text = paste0(paste("Product: ",product),
                        '<br>',
                        '<br>',          
                        '<sup>',
                        paste("Product ID: ",toString(prod),", Retailer ID: ",product(prod)$retailer_id),
                        '</sup>')),
           xaxis = list(title="Date"),
           yaxis = list(title="Price")         
          )

    return(fig)
}

plot_prod_variability(1386116)